TY  - JOUR
T1  - Classification of Feature Extracted, Selected and Segmented Mammogram Image Using Hybrid Algorithm-Monkey Search Optimization (MSO) and Support Vector Machine (SVM)
AU - Suguna, S. Kanimozhi AU - Maheswari, S. Uma 
JO  - Research Journal of Applied Sciences
VL  - 9
IS  - 2
SP  - 110
EP  - 118
PY  - 2014
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2014.110.118
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2014.110.118
KW  - MSO
KW  -climb
KW  -watch and jump
KW  -cooperation
KW  -somersault
KW  -stochastic perturbation mechanism
KW  -termination
AB  - Classification is most important for analyzing the mammograms of breast cancer. This study proposes a different approach based on Metaheuristic Algorithm is presented for classifying the mammogram image. The foraging behavior of monkey is optimized as Monkey Search Optimization (MSO) which is the subset of the metaheuristic algorithm. Feature extracted image is given as input for the process of classification. To solve complex problems by cooperation the behaviors are considered. Several algorithms based on population-based metaheuristic algorithms were introduced in the literatures to solve different problems like optimization problems. This is the new technique proposed for classifying the mammogram images. Results are presented based on simulation made with the implementation in MATLAB which is tested on the images of MIAS database.
ER  -